Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations152
Missing cells150
Missing cells (%)5.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.7 KiB
Average record size in memory152.9 B

Variable types

Numeric15
Categorical2
DateTime2

Alerts

centr_2_turb_cf is highly overall correlated with turb_fin_cultivo_cfHigh correlation
dur_cf is highly overall correlated with orden_encadenado_cfHigh correlation
id_centr is highly overall correlated with lote and 1 other fieldsHigh correlation
lote is highly overall correlated with id_centr and 3 other fieldsHigh correlation
lote_parental_cf is highly overall correlated with lote and 4 other fieldsHigh correlation
orden is highly overall correlated with id_centr and 3 other fieldsHigh correlation
orden_encadenado_cf is highly overall correlated with dur_cf and 3 other fieldsHigh correlation
producto_1_cf is highly overall correlated with producto_2_cfHigh correlation
producto_2_cf is highly overall correlated with producto_1_cfHigh correlation
turb_fin_cultivo_cf is highly overall correlated with centr_2_turb_cf and 1 other fieldsHigh correlation
turb_inicio_cultivo_cf is highly overall correlated with lote_parental_cfHigh correlation
turbidez_diff_cf is highly overall correlated with turb_fin_cultivo_cfHigh correlation
vol_ino_util_cf is highly overall correlated with lote_parental_cfHigh correlation
orden_encadenado_cf is highly imbalanced (55.1%)Imbalance
lote_parental_cf has 130 (85.5%) missing valuesMissing
vol_ino_util_cf has 5 (3.3%) missing valuesMissing
centr_1_turb_cf has 4 (2.6%) missing valuesMissing
centr_2_turb_cf has 9 (5.9%) missing valuesMissing
lote has unique valuesUnique
cantidad_of has 2 (1.3%) zerosZeros

Reproduction

Analysis started2024-10-13 15:45:54.519205
Analysis finished2024-10-13 15:46:21.566207
Duration27.05 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

lote
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct152
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23323.151
Minimum23019
Maximum24053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:21.616672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum23019
5-th percentile23026.55
Q123060.75
median23101.5
Q324003.25
95-th percentile24044.45
Maximum24053
Range1034
Interquartile range (IQR)942.5

Descriptive statistics

Standard deviation416.71493
Coefficient of variation (CV)0.017867008
Kurtosis-0.75084625
Mean23323.151
Median Absolute Deviation (MAD)43
Skewness1.1084266
Sum3545119
Variance173651.33
MonotonicityNot monotonic
2024-10-13T17:46:21.690039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23019 1
 
0.7%
23131 1
 
0.7%
23123 1
 
0.7%
23124 1
 
0.7%
23125 1
 
0.7%
23126 1
 
0.7%
23127 1
 
0.7%
23129 1
 
0.7%
23130 1
 
0.7%
23132 1
 
0.7%
Other values (142) 142
93.4%
ValueCountFrequency (%)
23019 1
0.7%
23020 1
0.7%
23021 1
0.7%
23022 1
0.7%
23023 1
0.7%
23024 1
0.7%
23025 1
0.7%
23026 1
0.7%
23027 1
0.7%
23028 1
0.7%
ValueCountFrequency (%)
24053 1
0.7%
24052 1
0.7%
24051 1
0.7%
24050 1
0.7%
24049 1
0.7%
24047 1
0.7%
24046 1
0.7%
24045 1
0.7%
24044 1
0.7%
24043 1
0.7%

orden_encadenado_cf
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1
127 
2
23 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters152
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Length

2024-10-13T17:46:21.754636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-13T17:46:21.809181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

lote_parental_cf
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing130
Missing (%)85.5%
Infinite0
Infinite (%)0.0%
Mean23571.818
Minimum23085
Maximum24051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:21.861356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum23085
5-th percentile23099.05
Q123112.25
median23567.5
Q324034.75
95-th percentile24049.7
Maximum24051
Range966
Interquartile range (IQR)922.5

Descriptive statistics

Standard deviation472.6577
Coefficient of variation (CV)0.020051813
Kurtosis-2.2071206
Mean23571.818
Median Absolute Deviation (MAD)459.5
Skewness0.00017902579
Sum518580
Variance223405.3
MonotonicityNot monotonic
2024-10-13T17:46:21.920899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
24010 1
 
0.7%
24050 1
 
0.7%
24044 1
 
0.7%
24041 1
 
0.7%
24036 1
 
0.7%
24037 1
 
0.7%
24031 1
 
0.7%
24027 1
 
0.7%
24021 1
 
0.7%
24020 1
 
0.7%
Other values (12) 12
 
7.9%
(Missing) 130
85.5%
ValueCountFrequency (%)
23085 1
0.7%
23099 1
0.7%
23100 1
0.7%
23108 1
0.7%
23109 1
0.7%
23112 1
0.7%
23113 1
0.7%
23118 1
0.7%
23119 1
0.7%
23124 1
0.7%
ValueCountFrequency (%)
24051 1
0.7%
24050 1
0.7%
24044 1
0.7%
24041 1
0.7%
24037 1
0.7%
24036 1
0.7%
24031 1
0.7%
24027 1
0.7%
24021 1
0.7%
24020 1
0.7%

id_bio
Real number (ℝ)

Distinct7
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14120.803
Minimum13169
Maximum14617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:21.968734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum13169
5-th percentile13169
Q113170
median14614
Q314616
95-th percentile14617
Maximum14617
Range1448
Interquartile range (IQR)1446

Descriptive statistics

Standard deviation687.94411
Coefficient of variation (CV)0.048718485
Kurtosis-1.5687935
Mean14120.803
Median Absolute Deviation (MAD)2
Skewness-0.67230589
Sum2146362
Variance473267.1
MonotonicityNot monotonic
2024-10-13T17:46:22.020646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
14616 34
22.4%
14615 30
19.7%
13170 29
19.1%
14614 27
17.8%
13169 22
14.5%
14617 9
 
5.9%
13189 1
 
0.7%
ValueCountFrequency (%)
13169 22
14.5%
13170 29
19.1%
13189 1
 
0.7%
14614 27
17.8%
14615 30
19.7%
14616 34
22.4%
14617 9
 
5.9%
ValueCountFrequency (%)
14617 9
 
5.9%
14616 34
22.4%
14615 30
19.7%
14614 27
17.8%
13189 1
 
0.7%
13170 29
19.1%
13169 22
14.5%
Distinct103
Distinct (%)67.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-03-21 06:30:00+00:00
Maximum2024-03-25 12:28:00+00:00
2024-10-13T17:46:22.096041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:22.283704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct137
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-03-23 05:30:00+00:00
Maximum2024-03-27 07:51:00+00:00
2024-10-13T17:46:22.358688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:22.432312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

vol_ino_util_cf
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)19.7%
Missing5
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean81.458503
Minimum66.4
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:22.499171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum66.4
5-th percentile79.44
Q180
median81.6
Q382.8
95-th percentile84.16
Maximum88
Range21.6
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.248108
Coefficient of variation (CV)0.027598199
Kurtosis13.302453
Mean81.458503
Median Absolute Deviation (MAD)1.6
Skewness-1.7398503
Sum11974.4
Variance5.0539895
MonotonicityNot monotonic
2024-10-13T17:46:22.555269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
80 44
28.9%
82.4 12
 
7.9%
83.2 12
 
7.9%
81.6 11
 
7.2%
82 11
 
7.2%
80.8 7
 
4.6%
84 7
 
4.6%
83.6 6
 
3.9%
81.2 6
 
3.9%
80.4 5
 
3.3%
Other values (19) 26
17.1%
ValueCountFrequency (%)
66.4 1
 
0.7%
76 1
 
0.7%
77.2 1
 
0.7%
77.6 2
 
1.3%
78.4 1
 
0.7%
78.8 1
 
0.7%
79.2 1
 
0.7%
80 44
28.9%
80.4 5
 
3.3%
80.56 1
 
0.7%
ValueCountFrequency (%)
88 1
 
0.7%
87.2 1
 
0.7%
86.4 1
 
0.7%
85.92 1
 
0.7%
85.6 1
 
0.7%
85.2 1
 
0.7%
84.16 3
 
2.0%
84 7
4.6%
83.6 6
3.9%
83.2 12
7.9%

turb_inicio_cultivo_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.036316
Minimum12.56
Maximum44.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:22.614771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12.56
5-th percentile14.8
Q116.4
median17.76
Q318.8
95-th percentile21.796
Maximum44.4
Range31.84
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation3.3008587
Coefficient of variation (CV)0.18301181
Kurtosis28.581161
Mean18.036316
Median Absolute Deviation (MAD)1.16
Skewness4.248607
Sum2741.52
Variance10.895668
MonotonicityNot monotonic
2024-10-13T17:46:22.685384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.84 6
 
3.9%
16.16 5
 
3.3%
16.64 5
 
3.3%
18.32 5
 
3.3%
17.76 5
 
3.3%
18 4
 
2.6%
18.72 4
 
2.6%
15.28 4
 
2.6%
17.6 4
 
2.6%
17.12 4
 
2.6%
Other values (62) 106
69.7%
ValueCountFrequency (%)
12.56 1
0.7%
13.36 1
0.7%
14.08 1
0.7%
14.4 1
0.7%
14.48 1
0.7%
14.56 2
1.3%
14.8 2
1.3%
14.88 2
1.3%
14.96 1
0.7%
15.04 1
0.7%
ValueCountFrequency (%)
44.4 1
0.7%
30.32 2
1.3%
27.04 1
0.7%
26.24 1
0.7%
23.2 1
0.7%
22 1
0.7%
21.84 1
0.7%
21.76 1
0.7%
21.44 1
0.7%
20.8 2
1.3%

turb_fin_cultivo_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.416316
Minimum42.8
Maximum91.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:22.754893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum42.8
5-th percentile59.828
Q169.1
median74.32
Q381.08
95-th percentile87.2
Maximum91.2
Range48.4
Interquartile range (IQR)11.98

Descriptive statistics

Standard deviation8.9408989
Coefficient of variation (CV)0.12014702
Kurtosis1.2364086
Mean74.416316
Median Absolute Deviation (MAD)5.64
Skewness-0.75227869
Sum11311.28
Variance79.939674
MonotonicityNot monotonic
2024-10-13T17:46:22.826137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 7
 
4.6%
83.2 6
 
3.9%
81.6 5
 
3.3%
80.8 4
 
2.6%
85.6 4
 
2.6%
74.4 3
 
2.0%
87.2 3
 
2.0%
69.04 3
 
2.0%
72.48 3
 
2.0%
73.52 2
 
1.3%
Other values (96) 112
73.7%
ValueCountFrequency (%)
42.8 1
0.7%
44.32 1
0.7%
49.36 1
0.7%
49.76 1
0.7%
54.16 1
0.7%
56.48 1
0.7%
56.96 1
0.7%
59.52 1
0.7%
60.08 1
0.7%
60.72 1
0.7%
ValueCountFrequency (%)
91.2 2
 
1.3%
90.4 2
 
1.3%
89.6 1
 
0.7%
88 1
 
0.7%
87.2 3
2.0%
86.4 2
 
1.3%
85.6 4
2.6%
84.8 2
 
1.3%
84 7
4.6%
83.2 6
3.9%

viab_final_cultivo_cf
Real number (ℝ)

Distinct101
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7016579 × 108
Minimum70400000
Maximum3.696 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:22.892903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum70400000
5-th percentile1.1448 × 108
Q11.48 × 108
median1.652 × 108
Q31.922 × 108
95-th percentile2.2828 × 108
Maximum3.696 × 108
Range2.992 × 108
Interquartile range (IQR)44200000

Descriptive statistics

Standard deviation38308279
Coefficient of variation (CV)0.22512327
Kurtosis4.5153204
Mean1.7016579 × 108
Median Absolute Deviation (MAD)20400000
Skewness0.99890815
Sum2.58652 × 1010
Variance1.4675242 × 1015
MonotonicityNot monotonic
2024-10-13T17:46:22.963635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195200000 5
 
3.3%
164000000 4
 
2.6%
145600000 4
 
2.6%
185600000 3
 
2.0%
157600000 3
 
2.0%
158400000 3
 
2.0%
163200000 3
 
2.0%
153600000 3
 
2.0%
156800000 3
 
2.0%
184000000 3
 
2.0%
Other values (91) 118
77.6%
ValueCountFrequency (%)
70400000 1
0.7%
91200000 1
0.7%
95200000 1
0.7%
97600000 1
0.7%
100000000 1
0.7%
101600000 1
0.7%
104000000 1
0.7%
113600000 1
0.7%
115200000 1
0.7%
117600000 1
0.7%
ValueCountFrequency (%)
369600000 1
0.7%
280000000 1
0.7%
262400000 1
0.7%
260000000 1
0.7%
248000000 1
0.7%
240000000 1
0.7%
232000000 1
0.7%
229600000 1
0.7%
227200000 1
0.7%
224000000 1
0.7%

id_centr
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
14246
60 
17825
54 
12912
36 
6379
 
2

Length

Max length5
Median length5
Mean length4.9868421
Min length4

Characters and Unicode

Total characters758
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row17825
2nd row14246
3rd row17825
4th row12912
5th row17825

Common Values

ValueCountFrequency (%)
14246 60
39.5%
17825 54
35.5%
12912 36
23.7%
6379 2
 
1.3%

Length

2024-10-13T17:46:23.033097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-13T17:46:23.090590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
14246 60
39.5%
17825 54
35.5%
12912 36
23.7%
6379 2
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

centr_1_turb_cf
Real number (ℝ)

MISSING 

Distinct92
Distinct (%)62.2%
Missing4
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean30.067703
Minimum21.28
Maximum168.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:23.154131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum21.28
5-th percentile23.096
Q126.44
median28.56
Q330.5
95-th percentile33.44
Maximum168.8
Range147.52
Interquartile range (IQR)4.06

Descriptive statistics

Standard deviation15.167552
Coefficient of variation (CV)0.50444664
Kurtosis67.864839
Mean30.067703
Median Absolute Deviation (MAD)2.04
Skewness8.063087
Sum4450.02
Variance230.05462
MonotonicityNot monotonic
2024-10-13T17:46:23.225915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.84 7
 
4.6%
28.72 5
 
3.3%
29.44 4
 
2.6%
28.56 4
 
2.6%
30.4 4
 
2.6%
26.56 4
 
2.6%
27.44 3
 
2.0%
30.16 3
 
2.0%
31.76 3
 
2.0%
29.52 3
 
2.0%
Other values (82) 108
71.1%
(Missing) 4
 
2.6%
ValueCountFrequency (%)
21.28 1
0.7%
21.52 1
0.7%
21.76 1
0.7%
21.84 1
0.7%
22.08 1
0.7%
22.4 1
0.7%
22.64 1
0.7%
23.04 1
0.7%
23.2 1
0.7%
23.28 1
0.7%
ValueCountFrequency (%)
168.8 1
 
0.7%
142.4 1
 
0.7%
40.9 1
 
0.7%
36.64 1
 
0.7%
34.48 1
 
0.7%
34 1
 
0.7%
33.6 1
 
0.7%
33.44 3
2.0%
33.2 1
 
0.7%
32.72 1
 
0.7%

centr_2_turb_cf
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct112
Distinct (%)78.3%
Missing9
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean23.56979
Minimum9.84
Maximum156.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:23.296145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9.84
5-th percentile12.2
Q117.72
median20.72
Q325
95-th percentile37.704
Maximum156.96
Range147.12
Interquartile range (IQR)7.28

Descriptive statistics

Standard deviation17.21646
Coefficient of variation (CV)0.73044604
Kurtosis46.177047
Mean23.56979
Median Absolute Deviation (MAD)3.84
Skewness6.3247727
Sum3370.48
Variance296.4065
MonotonicityNot monotonic
2024-10-13T17:46:23.366970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 4
 
2.6%
21.36 4
 
2.6%
20.8 4
 
2.6%
17.76 3
 
2.0%
19.52 3
 
2.0%
20.88 2
 
1.3%
17.84 2
 
1.3%
22.24 2
 
1.3%
15.36 2
 
1.3%
20.72 2
 
1.3%
Other values (102) 115
75.7%
(Missing) 9
 
5.9%
ValueCountFrequency (%)
9.84 1
0.7%
10.08 1
0.7%
10.4 2
1.3%
11.44 1
0.7%
11.6 2
1.3%
12.16 1
0.7%
12.56 1
0.7%
12.88 1
0.7%
13.2 1
0.7%
13.36 1
0.7%
ValueCountFrequency (%)
156.96 1
0.7%
151.76 1
0.7%
54.8 1
0.7%
49.04 1
0.7%
44.4 1
0.7%
44 1
0.7%
38.4 1
0.7%
37.84 1
0.7%
36.48 1
0.7%
34.48 1
0.7%

producto_1_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct150
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1658.3157
Minimum526.4
Maximum2395.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:23.436687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum526.4
5-th percentile1175.112
Q11466.76
median1675.4
Q31853.798
95-th percentile2140.896
Maximum2395.36
Range1868.96
Interquartile range (IQR)387.038

Descriptive statistics

Standard deviation307.71306
Coefficient of variation (CV)0.18555758
Kurtosis0.45810105
Mean1658.3157
Median Absolute Deviation (MAD)197.08
Skewness-0.34206353
Sum252063.99
Variance94687.327
MonotonicityNot monotonic
2024-10-13T17:46:23.509081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1468.88 2
 
1.3%
1517.92 2
 
1.3%
1747.92 1
 
0.7%
1902.96 1
 
0.7%
1978.16 1
 
0.7%
2117.76 1
 
0.7%
1688.08 1
 
0.7%
2395.36 1
 
0.7%
2155.76 1
 
0.7%
1116.64 1
 
0.7%
Other values (140) 140
92.1%
ValueCountFrequency (%)
526.4 1
0.7%
969.888 1
0.7%
970.8 1
0.7%
988.096 1
0.7%
1096.584 1
0.7%
1101.04 1
0.7%
1116.64 1
0.7%
1151.44 1
0.7%
1194.48 1
0.7%
1198.16 1
0.7%
ValueCountFrequency (%)
2395.36 1
0.7%
2338.56 1
0.7%
2263.2 1
0.7%
2162.48 1
0.7%
2161.12 1
0.7%
2155.76 1
0.7%
2151.536 1
0.7%
2150.576 1
0.7%
2132.976 1
0.7%
2129.92 1
0.7%

producto_2_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1209789
Minimum2.8
Maximum9.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:23.689294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile3.936
Q15.1
median6.08
Q37.12
95-th percentile8.312
Maximum9.2
Range6.4
Interquartile range (IQR)2.02

Descriptive statistics

Standard deviation1.4079732
Coefficient of variation (CV)0.23002418
Kurtosis-0.57667228
Mean6.1209789
Median Absolute Deviation (MAD)1.04
Skewness-0.0097291881
Sum930.3888
Variance1.9823884
MonotonicityNot monotonic
2024-10-13T17:46:23.756437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.16 6
 
3.9%
6.88 5
 
3.3%
6.56 5
 
3.3%
5.44 5
 
3.3%
5.52 5
 
3.3%
4.48 5
 
3.3%
5.76 4
 
2.6%
6.72 4
 
2.6%
5.28 4
 
2.6%
4.88 4
 
2.6%
Other values (54) 105
69.1%
ValueCountFrequency (%)
2.8 1
 
0.7%
2.96 1
 
0.7%
3.04 1
 
0.7%
3.44 2
1.3%
3.6 1
 
0.7%
3.68 1
 
0.7%
3.76 1
 
0.7%
4.08 2
1.3%
4.16 2
1.3%
4.24 3
2.0%
ValueCountFrequency (%)
9.2 1
 
0.7%
9.12 1
 
0.7%
8.96 1
 
0.7%
8.72 2
1.3%
8.64 1
 
0.7%
8.48 1
 
0.7%
8.4 1
 
0.7%
8.24 1
 
0.7%
8.16 2
1.3%
8.08 4
2.6%

dur_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173236.18
Minimum151200
Maximum193500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:23.827039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum151200
5-th percentile159600
Q1171045
median174150
Q3177300
95-th percentile182700
Maximum193500
Range42300
Interquartile range (IQR)6255

Descriptive statistics

Standard deviation7021.1053
Coefficient of variation (CV)0.040529092
Kurtosis0.60961578
Mean173236.18
Median Absolute Deviation (MAD)3150
Skewness-0.53540339
Sum26331900
Variance49295919
MonotonicityNot monotonic
2024-10-13T17:46:23.897651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172800 15
 
9.9%
176400 11
 
7.2%
178200 9
 
5.9%
174600 7
 
4.6%
169200 6
 
3.9%
171900 5
 
3.3%
180900 4
 
2.6%
171300 4
 
2.6%
175200 4
 
2.6%
162000 4
 
2.6%
Other values (57) 83
54.6%
ValueCountFrequency (%)
151200 1
 
0.7%
156180 1
 
0.7%
157200 1
 
0.7%
157500 1
 
0.7%
158400 2
1.3%
159300 1
 
0.7%
159600 3
2.0%
161100 2
1.3%
162000 4
2.6%
162300 1
 
0.7%
ValueCountFrequency (%)
193500 1
 
0.7%
189000 1
 
0.7%
187200 1
 
0.7%
185700 1
 
0.7%
185400 1
 
0.7%
183300 1
 
0.7%
182700 3
2.0%
181980 1
 
0.7%
181800 3
2.0%
181500 1
 
0.7%

turbidez_diff_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct138
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.38
Minimum24.72
Maximum73.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:23.963824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum24.72
5-th percentile41.688
Q151.04
median56.32
Q363.6
95-th percentile69.592
Maximum73.92
Range49.2
Interquartile range (IQR)12.56

Descriptive statistics

Standard deviation9.1640079
Coefficient of variation (CV)0.16254005
Kurtosis0.77084379
Mean56.38
Median Absolute Deviation (MAD)6.6
Skewness-0.62096502
Sum8569.76
Variance83.979041
MonotonicityNot monotonic
2024-10-13T17:46:24.031310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.48 3
 
2.0%
49.52 3
 
2.0%
65.2 2
 
1.3%
49.36 2
 
1.3%
48.08 2
 
1.3%
52.8 2
 
1.3%
68.16 2
 
1.3%
44.72 2
 
1.3%
54.16 2
 
1.3%
62.96 2
 
1.3%
Other values (128) 130
85.5%
ValueCountFrequency (%)
24.72 1
0.7%
29.84 1
0.7%
30.96 1
0.7%
31.12 1
0.7%
34.96 1
0.7%
37.2 1
0.7%
40.8 1
0.7%
41.6 1
0.7%
41.76 1
0.7%
42.4 1
0.7%
ValueCountFrequency (%)
73.92 1
0.7%
73.28 1
0.7%
72.48 1
0.7%
72.4 2
1.3%
70.64 1
0.7%
70.24 1
0.7%
69.68 1
0.7%
69.52 1
0.7%
69.44 1
0.7%
69.12 1
0.7%

orden
Real number (ℝ)

HIGH CORRELATION 

Distinct151
Distinct (%)100.0%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1.9515364 × 108
Minimum10005233
Maximum2.0020039 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:24.099488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10005233
5-th percentile2.0018243 × 108
Q12.001863 × 108
median2.0019139 × 108
Q32.0019688 × 108
95-th percentile2.0020037 × 108
Maximum2.0020039 × 108
Range1.9019516 × 108
Interquartile range (IQR)10588

Descriptive statistics

Standard deviation30643202
Coefficient of variation (CV)0.15702091
Kurtosis33.929818
Mean1.9515364 × 108
Median Absolute Deviation (MAD)5498
Skewness-5.9565554
Sum2.94682 × 1010
Variance9.3900583 × 1014
MonotonicityNot monotonic
2024-10-13T17:46:24.175361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200178572 1
 
0.7%
200196877 1
 
0.7%
200196590 1
 
0.7%
200196793 1
 
0.7%
200196794 1
 
0.7%
10005235 1
 
0.7%
10005236 1
 
0.7%
200196875 1
 
0.7%
200196872 1
 
0.7%
200196876 1
 
0.7%
Other values (141) 141
92.8%
ValueCountFrequency (%)
10005233 1
0.7%
10005235 1
0.7%
10005236 1
0.7%
10005271 1
0.7%
200178572 1
0.7%
200179217 1
0.7%
200181620 1
0.7%
200182428 1
0.7%
200182429 1
0.7%
200182430 1
0.7%
ValueCountFrequency (%)
200200388 1
0.7%
200200387 1
0.7%
200200386 1
0.7%
200200385 1
0.7%
200200381 1
0.7%
200200380 1
0.7%
200200378 1
0.7%
200200374 1
0.7%
200200373 1
0.7%
200200372 1
0.7%

cantidad_of
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)20.5%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean13.520662
Minimum0
Maximum14
Zeros2
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-13T17:46:24.240604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.5
Q113.645
median13.7
Q313.8
95-th percentile13.895
Maximum14
Range14
Interquartile range (IQR)0.155

Descriptive statistics

Standard deviation1.5786875
Coefficient of variation (CV)0.11676111
Kurtosis71.60626
Mean13.520662
Median Absolute Deviation (MAD)0.1
Skewness-8.4872312
Sum2041.62
Variance2.4922542
MonotonicityNot monotonic
2024-10-13T17:46:24.303640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13.7 45
29.6%
13.8 32
21.1%
13.6 19
12.5%
13.9 6
 
3.9%
13.75 5
 
3.3%
13.5 5
 
3.3%
13.72 3
 
2.0%
13.77 3
 
2.0%
13.76 2
 
1.3%
0 2
 
1.3%
Other values (21) 29
19.1%
ValueCountFrequency (%)
0 2
 
1.3%
12.48 1
 
0.7%
13.3 1
 
0.7%
13.31 1
 
0.7%
13.4 1
 
0.7%
13.5 5
3.3%
13.54 2
 
1.3%
13.55 2
 
1.3%
13.57 1
 
0.7%
13.59 1
 
0.7%
ValueCountFrequency (%)
14 2
 
1.3%
13.9 6
 
3.9%
13.89 1
 
0.7%
13.86 1
 
0.7%
13.83 1
 
0.7%
13.82 1
 
0.7%
13.81 1
 
0.7%
13.8 32
21.1%
13.78 2
 
1.3%
13.77 3
 
2.0%

Interactions

2024-10-13T17:46:19.877213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:54.747049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:58.790783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:00.475097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:01.849298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:03.183154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:04.574792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:05.820896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:07.210734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:08.603933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:09.860735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:11.272282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:12.660930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:14.027904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:15.281991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:20.186653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:55.297776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:59.229704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:00.898312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:02.154796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:03.497477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:04.883691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:06.242621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:07.516987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:08.899356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:10.283317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:11.586625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:12.971657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:14.341055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:15.899005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:20.261486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:55.533331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:59.319379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:00.970543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:02.222174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:03.571661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:04.957462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:06.317489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:07.593301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:08.974652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:10.357963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:11.661804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:13.050082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:14.413732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:16.167058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:20.303608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:55.732884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:59.388885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:01.008713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:02.260950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:03.612927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:04.996307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:06.358974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:07.635352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:09.016283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:10.401197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:11.701458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:13.090954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:14.452281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:16.402110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:20.346271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:55.928300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:59.446274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:01.047494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:02.298101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:03.653980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:05.034554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:06.400096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:07.683218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:09.063798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:10.443469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:11.741126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:13.131042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:14.491405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:16.633316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:20.392282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:56.235300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:59.511982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:01.094824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:02.344150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:03.698304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:05.079593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:06.447639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:07.730412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:09.112133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:10.494469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:11.907261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:13.176542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:14.535875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:16.994625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:20.435199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:56.437870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:59.574629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:01.136328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:02.386789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:03.743483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:05.119871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:06.490247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:07.775319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:09.155918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:10.545448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:11.949186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:13.218177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:14.578213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:17.236413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:20.589195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:56.643026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:59.640514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:01.181649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:02.428752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:03.789536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:05.164738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:06.534788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:07.821245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:09.203882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:10.592626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:11.994735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:13.263797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:14.621692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:17.477519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:20.637022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:56.845891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:59.708379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:01.229658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:02.476934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:03.839803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:05.210722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:06.581948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:07.868968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:09.251853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:10.644393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:12.042802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:13.310174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:14.667261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:17.715948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:20.685109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:57.145263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:59.776338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:01.274974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:02.638558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:03.889032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:05.258248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:06.631429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:07.918004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:09.299958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:10.692431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:12.091468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:13.358343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:14.714433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:18.054677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:20.733575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:57.356353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:59.843022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:01.322773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:02.683258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:03.939121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:05.305231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:06.678980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:07.967041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:09.348744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:10.740684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:12.138784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:13.404296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:14.760179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:18.300659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:20.779573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:57.561214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:59.909342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:01.367040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:02.726236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:03.985394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:05.347910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:06.732769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:08.014419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:09.396428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:10.788210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:12.183242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:13.449405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:14.805353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:18.542038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:20.822367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:57.763130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:59.972517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:01.409710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:02.765302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:04.029506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:05.391977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:06.775912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:08.058014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:09.440193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:10.833469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:12.224725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:13.487862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:14.846255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:18.782917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:20.873000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:58.074459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:00.036340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:01.451271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:02.806462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:04.073012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:05.432452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:06.818296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:08.102973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:09.484627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:10.877753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:12.266267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:13.530275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:14.888164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:19.022407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:21.219950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:45:58.581684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:00.404785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:01.801767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:03.138274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:04.527149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:05.775348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:07.161290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:08.554126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:09.812363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:11.223756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:12.612176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:13.980726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:15.235187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-13T17:46:19.634703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-10-13T17:46:24.358639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
cantidad_ofcentr_1_turb_cfcentr_2_turb_cfdur_cfid_bioid_centrlotelote_parental_cfordenorden_encadenado_cfproducto_1_cfproducto_2_cfturb_fin_cultivo_cfturb_inicio_cultivo_cfturbidez_diff_cfviab_final_cultivo_cfvol_ino_util_cf
cantidad_of1.000-0.162-0.077-0.009-0.1040.000-0.1670.091-0.1350.0000.0180.067-0.022-0.0050.011-0.1730.059
centr_1_turb_cf-0.1621.0000.356-0.199-0.2020.0000.393-0.0890.3580.0000.037-0.0190.4070.4740.280-0.004-0.027
centr_2_turb_cf-0.0770.3561.000-0.302-0.1760.0170.228-0.1370.1210.0000.1940.4280.5060.0890.4670.141-0.073
dur_cf-0.009-0.199-0.3021.0000.1870.000-0.334-0.429-0.3130.587-0.135-0.267-0.461-0.370-0.328-0.2010.167
id_bio-0.104-0.202-0.1760.1871.0000.220-0.178-0.247-0.1210.064-0.087-0.193-0.193-0.062-0.158-0.1170.029
id_centr0.0000.0000.0170.0000.2201.0001.0000.0001.0000.0000.0440.1260.0000.0000.0000.2240.000
lote-0.1670.3930.228-0.334-0.1781.0001.0000.9980.9121.000-0.2200.0360.1940.1950.133-0.082-0.173
lote_parental_cf0.091-0.089-0.137-0.429-0.2470.0000.9981.0000.8100.748-0.102-0.1530.436-0.5300.467-0.3790.521
orden-0.1350.3580.121-0.313-0.1211.0000.9120.8101.0001.000-0.235-0.0120.1220.2190.055-0.153-0.111
orden_encadenado_cf0.0000.0000.0000.5870.0640.0001.0000.7481.0001.0000.0000.1830.0000.4020.0000.0000.270
producto_1_cf0.0180.0370.194-0.135-0.0870.044-0.220-0.102-0.2350.0001.0000.5130.4320.0630.4330.128-0.105
producto_2_cf0.067-0.0190.428-0.267-0.1930.1260.036-0.153-0.0120.1830.5131.0000.4980.0660.4960.079-0.227
turb_fin_cultivo_cf-0.0220.4070.506-0.461-0.1930.0000.1940.4360.1220.0000.4320.4981.0000.2190.9440.286-0.294
turb_inicio_cultivo_cf-0.0050.4740.089-0.370-0.0620.0000.195-0.5300.2190.4020.0630.0660.2191.000-0.040-0.056-0.103
turbidez_diff_cf0.0110.2800.467-0.328-0.1580.0000.1330.4670.0550.0000.4330.4960.944-0.0401.0000.276-0.236
viab_final_cultivo_cf-0.173-0.0040.141-0.201-0.1170.224-0.082-0.379-0.1530.0000.1280.0790.286-0.0560.2761.000-0.024
vol_ino_util_cf0.059-0.027-0.0730.1670.0290.000-0.1730.521-0.1110.270-0.105-0.227-0.294-0.103-0.236-0.0241.000

Missing values

2024-10-13T17:46:21.294572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-13T17:46:21.428835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-13T17:46:21.524311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

loteorden_encadenado_cflote_parental_cfid_biof_h_inicio_cff_h_fin_cfvol_ino_util_cfturb_inicio_cultivo_cfturb_fin_cultivo_cfviab_final_cultivo_cfid_centrcentr_1_turb_cfcentr_2_turb_cfproducto_1_cfproducto_2_cfdur_cfturbidez_diff_cfordencantidad_of
0230191nan146152023-03-21 06:30:00+00:002023-03-23 05:30:00+00:0082.417.2891.20184000000.017825NaNNaN1747.9206.00169200.073.9220017857213.8
1230201nan146162023-03-21 06:30:00+00:002023-03-23 05:30:00+00:0080.418.8091.20181600000.014246NaNNaN1676.1606.56169200.072.4020017921713.6
2230211nan131702023-03-22 06:30:00+00:002023-03-24 05:30:00+00:0066.416.1686.40248000000.017825NaNNaN1928.4968.08169200.070.2420018162013.5
3230221nan146142023-03-22 06:30:00+00:002023-03-24 05:30:00+00:0085.618.4883.20229600000.012912NaNNaN1782.8005.92169200.064.7220018242813.8
4230231nan146152023-03-28 05:27:00+00:002023-03-30 08:00:00+00:0077.617.1274.40132800000.01782526.5620.881861.8402.96181980.057.2820018242913.7
5230241nan146162023-03-28 05:24:00+00:002023-03-30 05:23:00+00:0076.016.5680.80199200000.01424624.5610.402161.1202.80172740.064.2420018243013.7
6230251nan131702023-03-29 05:09:00+00:002023-03-31 05:29:00+00:0077.217.7687.20199200000.01782530.6429.362044.7204.48174000.069.4420018243113.7
7230261nan146142023-03-29 05:29:00+00:002023-03-31 05:38:00+00:0078.818.2481.20206400000.01424626.4811.602263.2003.44173340.062.9620018243213.8
8230271nan146152023-04-04 08:32:00+00:002023-04-06 10:30:00+00:0083.216.8868.08195200000.01424626.249.841407.6804.08179880.051.2020018243313.7
9230281nan146162023-04-04 08:34:00+00:002023-04-06 10:32:00+00:0083.618.5667.20176000000.01782527.2812.161373.2004.72179880.048.6420018243413.7
loteorden_encadenado_cflote_parental_cfid_biof_h_inicio_cff_h_fin_cfvol_ino_util_cfturb_inicio_cultivo_cfturb_fin_cultivo_cfviab_final_cultivo_cfid_centrcentr_1_turb_cfcentr_2_turb_cfproducto_1_cfproducto_2_cfdur_cfturbidez_diff_cfordencantidad_of
14224046224041.0131692024-03-11 12:10:00+00:002024-03-13 09:50:00+00:0080.0019.9288.00156800000.01291232.7228.321783.847.84164400.068.0820020038713.71
143240431nan146142024-03-12 06:25:00+00:002024-03-14 07:25:00+00:0080.0019.4469.68132800000.01424630.3216.161254.564.72176400.050.24NaNNaN
144240451nan146162024-03-12 06:25:00+00:002024-03-14 08:15:00+00:0080.0017.5272.48139200000.01291227.8417.761573.525.76179400.054.9620020037413.71
145240441nan131702024-03-16 08:20:00+00:002024-03-18 07:01:00+00:0083.6019.2877.52160800000.01424630.7220.681528.725.44168060.058.2420020023313.75
14624047224044.0131702024-03-18 12:00:00+00:002024-03-20 06:00:00+00:0080.0018.2486.40223200000.01424628.1626.761794.326.64151200.068.1620020037813.72
147240491nan146172024-03-16 08:22:00+00:002024-03-18 07:23:00+00:0083.6018.8872.64164800000.01291230.5617.001342.804.88169260.053.7620020038013.57
148240501nan146142024-03-23 07:57:00+00:002024-03-25 07:28:00+00:0084.1617.7667.60152000000.0637929.4426.641422.803.68171060.049.8420020038113.63
149240511nan131692024-03-23 07:57:00+00:002024-03-25 07:33:00+00:0084.1617.7680.80160800000.01291233.4419.321486.565.52171360.063.0420020038513.83
15024052224050.0146142024-03-25 12:28:00+00:002024-03-27 07:51:00+00:0086.4017.2869.04148000000.01424623.6818.201857.286.00156180.051.7620020038813.78
15124053224051.0131692024-03-25 11:27:00+00:002024-03-27 07:27:00+00:0087.2016.7279.36148000000.01291226.5619.161784.087.20158400.062.6420020038613.75